Template Credit: Adapted from templates made available by Dr. Jason Brownlee of Machine Learning Mastery.
For more information on this case study project, please consult Dr. Brownlee’s blog post at https://machinelearningmastery.com/standard-machine-learning-datasets/.
Dataset Used: Connectionist Bench (Sonar, Mines vs. Rocks) Data Set
ML Model: Classification, numeric inputs
The Sonar Dataset involves the prediction of whether or not an object is a mine or a rock given the strength of sonar returns at different angles. It is a binary (2-class) classification problem.
The baseline performance of predicting the most prevalent class is a classification accuracy of approximately 53%. Top results achieve a classification accuracy of approximately 88%.
The purpose of this project is to analyze a dataset using various machine learning algorithms and to document the steps using a template. The project aims to touch on the following areas:
- Document a regression predictive modeling problem end-to-end.
- Explore data transformation options for improving model performance
- Explore algorithm tuning techniques for improving model performance
- Explore using and tuning ensemble methods for improving model performance
The HTML formatted report can be found here on GitHub.